{"title":"ARHGAP9作为腹主动脉瘤关键诊断标志物的多组学鉴定及实验验证","authors":"Zhe Peng, Kun Li, Shile Wu, Baozhang Chen, Xiaonan Wang, Liang Chen, Xinsheng Wang, Hao Zhang, Biao Wu","doi":"10.1155/humu/7230083","DOIUrl":null,"url":null,"abstract":"<p>Abdominal aortic aneurysm (AAA) is a serious vascular condition that significantly endangers the lives of patients. Although there have been improvements in early detection and treatment methods, considerable challenges persist regarding the timely identification and evaluation of risk associated with this disease. Therefore, there is an immediate requirement for novel biomarkers that can enhance the early diagnosis and risk evaluation of AAA, thus allowing for more accurate and individualized medical interventions. In this study, we identified key diagnostic markers for AAA using various machine learning algorithms, and we explored the functions of these genes in AAA through gene enrichment analysis. A diagnostic model for AAA was constructed based on multiple machine learning algorithms, with the random forest algorithm highlighting the central role of ARHGAP9. In vitro experiments confirmed the influence of ARHGAP9 on vascular smooth muscle cells (VSMCs). Our findings indicate that the key genes identified are associated with the immune microenvironment and metabolism in AAA samples. The validated diagnostic model exhibited excellent predictive performance. Knockdown of ARHGAP9 significantly inhibited the proliferative capacity of VSMCs. In conclusion, our results suggest that ARHGAP9 may serve as a diagnostic and therapeutic marker for AAA.</p>","PeriodicalId":13061,"journal":{"name":"Human Mutation","volume":"2025 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/humu/7230083","citationCount":"0","resultStr":"{\"title\":\"Identification of ARHGAP9 as a Key Diagnostic Marker for Abdominal Aortic Aneurysm by Multiomics and Experimental Validation\",\"authors\":\"Zhe Peng, Kun Li, Shile Wu, Baozhang Chen, Xiaonan Wang, Liang Chen, Xinsheng Wang, Hao Zhang, Biao Wu\",\"doi\":\"10.1155/humu/7230083\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Abdominal aortic aneurysm (AAA) is a serious vascular condition that significantly endangers the lives of patients. Although there have been improvements in early detection and treatment methods, considerable challenges persist regarding the timely identification and evaluation of risk associated with this disease. Therefore, there is an immediate requirement for novel biomarkers that can enhance the early diagnosis and risk evaluation of AAA, thus allowing for more accurate and individualized medical interventions. In this study, we identified key diagnostic markers for AAA using various machine learning algorithms, and we explored the functions of these genes in AAA through gene enrichment analysis. A diagnostic model for AAA was constructed based on multiple machine learning algorithms, with the random forest algorithm highlighting the central role of ARHGAP9. In vitro experiments confirmed the influence of ARHGAP9 on vascular smooth muscle cells (VSMCs). Our findings indicate that the key genes identified are associated with the immune microenvironment and metabolism in AAA samples. The validated diagnostic model exhibited excellent predictive performance. Knockdown of ARHGAP9 significantly inhibited the proliferative capacity of VSMCs. In conclusion, our results suggest that ARHGAP9 may serve as a diagnostic and therapeutic marker for AAA.</p>\",\"PeriodicalId\":13061,\"journal\":{\"name\":\"Human Mutation\",\"volume\":\"2025 1\",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2025-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1155/humu/7230083\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Human Mutation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1155/humu/7230083\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Mutation","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/humu/7230083","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
Identification of ARHGAP9 as a Key Diagnostic Marker for Abdominal Aortic Aneurysm by Multiomics and Experimental Validation
Abdominal aortic aneurysm (AAA) is a serious vascular condition that significantly endangers the lives of patients. Although there have been improvements in early detection and treatment methods, considerable challenges persist regarding the timely identification and evaluation of risk associated with this disease. Therefore, there is an immediate requirement for novel biomarkers that can enhance the early diagnosis and risk evaluation of AAA, thus allowing for more accurate and individualized medical interventions. In this study, we identified key diagnostic markers for AAA using various machine learning algorithms, and we explored the functions of these genes in AAA through gene enrichment analysis. A diagnostic model for AAA was constructed based on multiple machine learning algorithms, with the random forest algorithm highlighting the central role of ARHGAP9. In vitro experiments confirmed the influence of ARHGAP9 on vascular smooth muscle cells (VSMCs). Our findings indicate that the key genes identified are associated with the immune microenvironment and metabolism in AAA samples. The validated diagnostic model exhibited excellent predictive performance. Knockdown of ARHGAP9 significantly inhibited the proliferative capacity of VSMCs. In conclusion, our results suggest that ARHGAP9 may serve as a diagnostic and therapeutic marker for AAA.
期刊介绍:
Human Mutation is a peer-reviewed journal that offers publication of original Research Articles, Methods, Mutation Updates, Reviews, Database Articles, Rapid Communications, and Letters on broad aspects of mutation research in humans. Reports of novel DNA variations and their phenotypic consequences, reports of SNPs demonstrated as valuable for genomic analysis, descriptions of new molecular detection methods, and novel approaches to clinical diagnosis are welcomed. Novel reports of gene organization at the genomic level, reported in the context of mutation investigation, may be considered. The journal provides a unique forum for the exchange of ideas, methods, and applications of interest to molecular, human, and medical geneticists in academic, industrial, and clinical research settings worldwide.